Spatio‐temporal variation of biotic factors underpins contemporary range dynamics of congeners
Bibliographic record
Abstract
Species' ranges are complex often exhibiting multidirectional shifts over space and time. Despite the strong fingerprint of recent historical climate change on species' distributions, biotic factors such as loss of vegetative habitat and the presence of potential competitors constitute important yet often overlooked drivers of range dynamics. Furthermore, short-term changes in environmental conditions can influence the underlying processes of local extinction and local colonization that drive range shifts, yet are rarely considered at broad scales. We used dynamic state-space occupancy models to test multiple hypotheses of the relative importance of major drivers of range shifts of Golden-winged Warblers (Vermivora chrysoptera) and Blue-winged Warblers (V. cyanoptera) between 1983 and 2012 across North America: warming temperatures; habitat changes; and occurrence of congeneric species, used here as proxy for biotic interactions. Dynamic occupancies for both species were most influenced by spatial relative to temporal variation in temperature and habitat. However, temporal variation in temperature anomalies and biotic interactions remained important. The two biotic factors considered, habitat change and biotic interactions, had the largest relative effect on estimated extinction rates followed by abiotic temperature anomalies. For the Golden-winged Warbler, the predicted presence of the Blue-winged Warbler, a hypothesized competitor, most influenced extinction probabilities, contributing to evidence supporting its role in site-level species replacement. Given the overall importance of biotic factors on range-wide dynamic occupancies, their consideration alongside abiotic factors should not be overlooked. Our results suggest that warming compounds the negative effect of habitat loss emphasizing species' need for habitat to adapt to a changing climate. Notably, even closely related species exhibited individual responses to abiotic and biotic factors considered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".